Conditional Monte Carlo
Subtitle: Gradient Estimation and Optimization Applications
By: by Michael Fu, Jian-Qiang Hu
Contributors: Hu, Jianqiang
Series: The Springer International Series in Engineering and Computer Science, Discrete Event Dynamic Systems -- 392; Springer International Series in Engineering and Computer Science, Discrete Event Dynamic Systems -- 392.
Publisher: Springer US
Published: 1997
Place of publication: Boston, MA
Language: eng
Format: [electronic resource] :
Number of pages: 399
ISBN-13: 9781461378891; 9781461562931
Subjects: Distribution (Probability theory); Mathematical optimization; Systems theory; Computational complexity; Computer science
Description: Conditional Monte Carlo: Gradient Estimation and Optimization Applications deals with various gradient estimation techniques of perturbation analysis based on the use of conditional expectation. The primary setting is discrete-event stochastic simulation. This book presents applications to queueing and inventory, and to other diverse areas such as financial derivatives, pricing and statistical quality control. To researchers already in the area, this book offers a unified perspective and adequately summarizes the state of the art. To researchers new to the area, this book offers a more systematic and accessible means of understanding the techniques without having to scour through the immense literature and learn a new set of notation with each paper. To practitioners, this book provides a number of diverse application areas that makes the intuition accessible without having to fully commit to understanding all the theoretical niceties. In sum, the objectives of this monograph are two-fold: to bring together many of the interesting developments in perturbation analysis based on conditioning under a more unified framework, and to illustrate the diversity of applications to which these techniques can be applied. Conditional Monte Carlo: Gradient Estimation and Optimization Applications is suitable as a secondary text for graduate level courses on stochastic simulations, and as a reference for researchers and practitioners in industry.
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